Summary Of Brian Christian S The Alignment Problem
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Summary of Brian Christian’s The Alignment Problem
Buy now to get the main key ideas from Brian Christian’s The Alignment Problem As machine-learning systems grow not only more prevalent, but also more powerful, humans want to ensure that they understand us and do what we want, eliminating the possibility of catastrophic divergence. In the field of computer science, this question is known as the alignment problem. In The Alignment Problem (2020), Brian Christian raises questions of safety and ethics in a world where humans are turning into machines and machines are turning into humans. He discusses tools that, through imitation, curiosity, inference, and shaping, exhibit human skills without being programmed to do so. The future of machine learning holds risks, but also great promise.
The Alignment Problem
'Vital reading. This is the book on artificial intelligence we need right now.' Mike Krieger, cofounder of Instagram Artificial intelligence is rapidly dominating every aspect of our modern lives influencing the news we consume, whether we get a mortgage, and even which friends wish us happy birthday. But as algorithms make ever more decisions on our behalf, how do we ensure they do what we want? And fairly? This conundrum - dubbed 'The Alignment Problem' by experts - is the subject of this timely and important book. From the AI program which cheats at computer games to the sexist algorithm behind Google Translate, bestselling author Brian Christian explains how, as AI develops, we rapidly approach a collision between artificial intelligence and ethics. If we stand by, we face a future with unregulated algorithms that propagate our biases - and worse - violate our most sacred values. Urgent and fascinating, this is an accessible primer to the most important issue facing AI researchers today.
AI-ML for Decision and Risk Analysis
Author: Louis Anthony Cox Jr.
language: en
Publisher: Springer Nature
Release Date: 2023-07-05
This book explains and illustrates recent developments and advances in decision-making and risk analysis. It demonstrates how artificial intelligence (AI) and machine learning (ML) have not only benefitted from classical decision analysis concepts such as expected utility maximization but have also contributed to making normative decision theory more useful by forcing it to confront realistic complexities. These include skill acquisition, uncertain and time-consuming implementation of intended actions, open-world uncertainties about what might happen next and what consequences actions can have, and learning to cope effectively with uncertain and changing environments. The result is a more robust and implementable technology for AI/ML-assisted decision-making. The book is intended to inform a wide audience in related applied areas and to provide a fun and stimulating resource for students, researchers, and academics in data science and AI-ML, decision analysis, and other closely linked academic fields. It will also appeal to managers, analysts, decision-makers, and policymakers in financial, health and safety, environmental, business, engineering, and security risk management.